Random fields play a central role in the analysis of spatially correlated data and, as a result, have a significant impact on a broad array of scientific applications. This paper studies the cepstral random field model, providing recursive formulas that connect the spatial cepstral coefficients to an equivalent moving-average random field, which facilitates easy computation of the autocovarianc...

Obtaining a compact, information-rich representation of the speech signal is an important first step in ASR. A large majority of ASR systems use some form of cepstral coefficients for this purpose. Computation of these cepstral coefficients typically includes several of the following steps: (1) Highfrequency preemphasis, using an FIR filter of the form y(k) = x(k) ax(k-1), with a taking values ...

Filter bank is the most common feature being employed in the research of the marginalisation approaches for robust speech recognition due to its simplicity in detecting the unreliable data in the frequency domain. In this paper, we propose a hybrid approach based on the marginalisation and the soft decision techniques that make use of the Mel-frequency cepstral coefficients (MFCCs) instead of f...

in this paper, a new spectral representation is introduced and applied to speech recognition. As the widely used LPC autocorrelation technique, it arises from an optimization approach that starts from a set of M+ 1 autocorrelations estimated from the signal samples. This new technique models the analytic spectrum (Fourier's transform of the causal autocorrelation sequence) by assuming that its ...

This paper introduces a new articulation rate filter and reports its combination with recently proposed constant Q cepstral coefficients (CQCCs) in their first application to automatic speaker verification (ASV). CQCC features are extracted with the constant Q transform (CQT), a perceptually-inspired alternative to Fourier-based approaches to time-frequency analysis. The CQT offers greater freq...

In this paper we have studied two information fusion approaches, namely feature vector concatenation and decision fusion, for the task of reducing error rates in a speaker verification system used in mismatched conditions. Three types of features are fused: Mel Frequency Cepstral Coefficients (MFCC), MFCC with Cepstral Mean Subtraction (CMS) and Maximum Auto-Correlation Values (MACV). We have u...

Automatic Accents Identification is very important for discussion especially within scope of speaker recognition. Some contribution of appropriate techniques uses in Music Recognition and Accent Identification may contributes in improving the recognition rate. Techniques in discussing on music genre identification or accents automatic identification and the combination of both processes still i...

Automatic speaker recognition (ASR) systems are the field of Human-machine interaction and scientists have been using feature extraction matching methods to analyze synthesize these signals. One most commonly used for is Mel Frequency Cepstral Coefficients (MFCCs). Recent researches show that MFCCs successful in processing voice signal with high accuracies. represents a sequence signal-specific...